ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)

Research output: Contribution to journalConference articleResearchpeer-review

  • Baoguang Shi
  • Cong Yao
  • Minghui Liao
  • Mingkun Yang
  • Pei Xu
  • Linyan Cui
  • Belongie, Serge
  • Shijian Lu
  • Xiang Bai

Chinese is the most widely used language in the world. Algorithms that read Chinese text in natural images facilitate applications of various kinds. Despite the large potential value, datasets and competitions in the past primarily focus on English, which bares very different characteristics than Chinese. This report introduces RCTW, a new competition that focuses on Chinese text reading. The competition features a large-scale dataset with over 12,000 annotated images. Two tasks, namely text localization and end-To-end recognition, are set up. The competition took place from January 20 to May 31, 2017. 23 valid submissions were received from 19 teams. This report includes dataset description, task definitions, evaluation protocols, and results summaries and analysis. Through this competition, we call for more future research on the Chinese text reading problem.

Original languageEnglish
JournalProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Pages (from-to)1429-1434
Number of pages6
ISSN1520-5363
DOIs
Publication statusPublished - 2 Jul 2017
Externally publishedYes
Event14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017 - Kyoto, Japan
Duration: 9 Nov 201715 Nov 2017

Conference

Conference14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017
CountryJapan
CityKyoto
Period09/11/201715/11/2017
Sponsoret al., FxPaL, Glory, Hitachi, Media Drive, Sansan

Bibliographical note

Funding Information:
ACKNOWLEDGMENT The challenge is supported in part by NSFC 61222308. The authors thank Dr. Fei Yin and Dr. Cheng-Lin Liu for their suggestions. The authors also thank Zhiyong Liu, Yang Yang, Zhiqiang Zhang, Rui Yu and Xuelei Zhang for their efforts in annotating the data.

Publisher Copyright:
© 2017 IEEE.

    Research areas

  • Competition, Dataset, Detection, Recognition, Text

ID: 301826449